Put AI to work. Keep the keys.
AI is fast, capable, and impossible to fully trust. OGIAM is the certified factory it works inside: badged at the door, given a defined job, watched on the floor, metered so it cannot run up a bill, and stopped the instant it steps out of line. The safety lives in the building, not in the AI’s good behavior.
You cannot just hand AI the keys.
An AI agent is like a brilliant new hire you can never fully vet. It is fast and it gets real work done, but it is a guessing machine, and you cannot be sure what it will do next. Give it your systems and your customers’ data on trust, and one wrong guess is a leaked secret, a deleted record, or a bill you never approved.
Most “AI safety” answers this with a well-written instruction: a paragraph asking the AI to be careful. That holds right up until the AI ignores it. The factory answers it differently. You do not hand it the keys at all.
A certified building your AI works inside.
Think of it the way you would think about letting a contractor onto a secure floor. They do not get a master key on day one. They are badged in, given a defined job, watched by the security on the floor, and shown the door the moment they do something they should not. OGIAM is that building, the security, and the accounting, built for AI. Five stations, and the AI is on the untrusted side of every one.
Badged at the door
Every agent gets an identity it has to prove before it can touch anything. No shared master keys, no anonymous access. You always know exactly which agent did what, and who owns it.
Given a defined job
An agent can only do the work its role allows, and every single action it tries is checked against your rules before it happens. Not once at the start, every time.
Watched on the floor
Decoys that nothing legitimate would ever touch sit throughout the building. The moment an agent trips one, it is cut off automatically. And every move it makes is written to a tamper-proof record.
Metered
Its spending is capped. A looping or hijacked agent cannot quietly run up a bill, because the meter stops it long before the invoice does.
Stopped on command
The instant something looks wrong, an agent's access is revoked and it is done. Contain first, investigate after, the same way a real security team clears a floor.
Every action, in every system the agent touches, passes through all five. Nothing is left to the AI’s good intentions.
A decision on every action.
This is the heart of the factory. The AI proposes what it wants to do; a fixed rule at the door decides. Allow, hold for a human, reshape into something safer, or refuse. The same request always gets the same answer, and every answer names the rule behind it. The AI can ask for anything. It can never talk its way past the door.
- AllowAuthorized in-role work runs, exactly as the agent expects.
- EscalateAnything risky (moving money, sending outbound, deleting, changing access) waits for a human to say yes.
- TransformWhen there is a safer version of the action, it is reshaped to fit the rules instead of refused.
- DenyOut-of-role requests and any grab for secrets are refused. The record names the rule that fired.
The rule is not another AI’s opinion. It is fixed policy, so it behaves the same every time and can be read back later. That is what makes a probabilistic guessing machine safe to let act: the boundary it acts through is not a guess.
Every agent works inside a hard limit on what it is allowed to do, and it cannot exceed it, even if the underlying model changes tomorrow. Anything that alters one of your systems comes back as a change your engineers review and approve, never an unreviewed write.
And every decision, allow or deny, is written to a tamper-proof record, so there is always a trustworthy account of what happened and why.
You ask in plain words. The AI proposes the next step. The gate decides. Approved steps run as you, capped by your own access. Everything is recorded. A longer job just loops the middle, with the gate deciding each step.
Safe does not mean useless.
The point of the factory is not to lock the AI in a box. It is to let it do the whole job a person would, across your connected tools, while every step stays governed. One plain sentence becomes a chain of real actions. The routine ones run; the one that reaches out to the world waits for you.
“Scan my calendar for next week, draft a prep task for every external meeting, pull recent news on each company, and make a tracked link for the deck.”
- Read the calendarAllow
Finds three external meetings next week.
- Create tasksAllow
Drafts one prep task per meeting.
- Look up the newsAllow
Pulls recent news on each company and attaches it.
- Make a tracked linkAllow
Generates a shareable link for the deck.
- Send emailWaits for you
Offers to email each organizer a heads-up.
Connect your tools once (Microsoft 365, Salesforce, your CRM, your repositories, or any REST or OAuth vendor) and the agent works across all of them, governed identically in every one. Already using an AI you like? Plug it in behind the gate with a key. You keep your AI, and you gain the controls and the record. No rip-and-replace.
Any AI. The same safety.
There is no lock-in to one AI company. Use the models built into the platform, or bring your own account and keys from whichever provider you prefer. Either way the rules are identical, because the rules live in the building, not in the model. As better or cheaper AI arrives, you switch, and your protection does not change.
Plug in your existing AI and keys. It keeps doing what it does; it just does it behind the gate now.
Prefer not to manage a provider? Use the models built in, governed exactly the same way.
The safety is a property of the factory, so the choice of AI becomes a detail you can change, not a decision you are stuck with.
An authority plane, not a wrapper prompt.
This is the line that matters. Most AI safety is a request the AI can ignore. OGIAM is a boundary it cannot cross. The AI sits on the untrusted side of every door, and a fixed rule at each one decides what actually reaches your systems.
Safety you ask for
- The rules are a paragraph in the AI's prompt asking it to behave.
- The AI is trusted to police itself.
- One bad guess or one clever jailbreak, and the rule is gone.
- Change the model, and the behavior changes with it.
Safety you enforce
- A fixed rule at each door decides yes or no. It is not another AI's opinion.
- The AI sits on the untrusted side and never approves its own action.
- It can ask for anything; it only reaches the real world through a gate it does not control.
- The protection holds no matter which AI is behind it.
Which protections are truly enforced, and which are still only advice, is tracked as a number the system itself checks. A control cannot quietly slip from enforced back to a suggestion without it showing. That is the difference between safety you can point at and safety you hope for.
Proof you can watch happen.
Trust should be something you can check, not something you assume. On demand, the factory runs a live self-check and shows every decision was correct, fast, and fully recorded. It is like a vault you can ask to demonstrate its lock in front of you. It also shows you, in plain numbers from your own account, how much AI-written work it checked, how much bad code it caught, how many hostile agents a decoy contained, and what your AI usage cost.
It replays real allowed, blocked, and held-for-approval actions and shows each one landed the way your policy says it should.
It shows the checks happen in the moment, so the guardrails never slow the work to a crawl.
It shows every action left a record in the tamper-proof log, with nothing missing and nothing quietly changed.
See the agents on your site, and what they are really doing.
Websites are visited by AI agents now, not only people. Some are helpful, like a customer’s assistant doing a real task. Some are hostile, like scrapers and automated attackers. Most defenses ask one blunt question, “bot or human?”, from a label the bad ones simply fake. OGIAM asks a better one: across a whole visit, how did it behave? Good agents get a welcome lane. The bad ones give themselves away.
- A known AI assistantIdentified, rule-respectingArrived→Read the rules→Read a few pagesproven
- An unnamed scraperIgnored the rules, harvested greedilyArrived→Skipped the rules→Tripped a hidden trapproven
- An automated proberLooked for a way inArrived→Guessed at hidden pages→Hunted for a loginproven
- Unrecognized automationBot-like, not enough to be sureArrived→Moved unusually fastlikely
Known, well-behaved agents get a clear, trusted path instead of being blocked along with everything else.
An agent that ignores the rules and springs a trap set only for it has near-certainly shown its hand.
Single events are noise. We stitch a visitor's whole journey together, so a pattern of intent becomes visible.
How an agent is built and equipped says more about what it is for than anything it claims about itself.
It watches and reports first. Nothing legitimate is turned away, so it is safe to switch on from day one.
The result is a clear, correlated record of who did what, the kind a security team or investigator can use.
A name in a request is trivial to fake; behavior across a whole visit is not. We read the path an agent takes and the way it is built and equipped, which is far harder to disguise and points at the operator behind it, not just the tool. As best we can tell, no one else profiles agents this way on the open web. And we are honest about the line: when an agent gives itself away with certainty, we say so; when it is a strong likelihood, we label it that way rather than dress it up as proof.
Put AI to work, safely.
The thread through all of it: AI should work inside the machine, not operate it. It checks in like an employee, is badged at the door, given a job, watched, metered, and stopped the instant it steps out of line. That is what makes AI safe to actually put to work, and it holds no matter which AI you use or how good the next one gets. And it runs both ways: the same building that governs the agents you run is what meets the agents that arrive from the open web, welcoming the good and turning the rest away by how they behave, so one standard covers your whole operation, inside and out.